The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
B C Meikap - One of the best experts on this subject based on the ideXlab platform.
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response surface modeling and optimization of chromium vi removal from aqueous solution using tamarind wood activated carbon in batch process
Journal of Hazardous Materials, 2009Co-Authors: J N Sahu, Jyotikusum Acharya, B C MeikapAbstract:Abstract The present paper discusses response surface methodology (RSM) as an efficient approach for Predictive model building and optimization of chromium adsorption on developed activated carbon. In this work the application of RSM is presented for optimizing the removal of Cr(VI) ions from aqua solutions using activated carbon as adsorbent. All experiments were performed according to statistical designs in order to develop the Predictive Regression models used for optimization. The optimization of adsorption of chromium on activated carbon was carried out to ensure a high adsorption efficiency at low adsorbent dose and high initial concentration of Cr(VI). While the goal of adsorption of chromium optimization was to improve adsorption conditions in batch process, i.e., to minimize the adsorbent dose and to increase the initial concentration of Cr(VI). In the adsorption experiments a laboratory developed Tamarind wood activated carbon made of chemical activation (zinc chloride) was used. A 2 4 full factorial central composite design experimental design was employed. Analysis of variance (ANOVA) showed a high coefficient of determination value ( R 2 = 0.928) and satisfactory prediction second-order Regression model was derived. Maximum chromium removal efficiency was predicted and experimentally validated. The optimum adsorbent dose, temperature, initial concentration of Cr(VI) and initial pH of the Cr(VI) solution were found to be 4.3 g/l, 32 °C, 20.15 mg/l and 5.41 respectively. Under optimal value of process parameters, high removal (>89%) was obtained for Cr(VI).
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response surface modeling and optimization of chromium vi removal from aqueous solution using tamarind wood activated carbon in batch process
Journal of Hazardous Materials, 2009Co-Authors: J N Sahu, Jyotikusum Acharya, B C MeikapAbstract:The present paper discusses response surface methodology (RSM) as an efficient approach for Predictive model building and optimization of chromium adsorption on developed activated carbon. In this work the application of RSM is presented for optimizing the removal of Cr(VI) ions from aqua solutions using activated carbon as adsorbent. All experiments were performed according to statistical designs in order to develop the Predictive Regression models used for optimization. The optimization of adsorption of chromium on activated carbon was carried out to ensure a high adsorption efficiency at low adsorbent dose and high initial concentration of Cr(VI). While the goal of adsorption of chromium optimization was to improve adsorption conditions in batch process, i.e., to minimize the adsorbent dose and to increase the initial concentration of Cr(VI). In the adsorption experiments a laboratory developed Tamarind wood activated carbon made of chemical activation (zinc chloride) was used. A 2(4) full factorial central composite design experimental design was employed. Analysis of variance (ANOVA) showed a high coefficient of determination value (R(2)=0.928) and satisfactory prediction second-order Regression model was derived. Maximum chromium removal efficiency was predicted and experimentally validated. The optimum adsorbent dose, temperature, initial concentration of Cr(VI) and initial pH of the Cr(VI) solution were found to be 4.3g/l, 32 degrees C, 20.15 mg/l and 5.41 respectively. Under optimal value of process parameters, high removal (>89%) was obtained for Cr(VI).
J N Sahu - One of the best experts on this subject based on the ideXlab platform.
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response surface modeling and optimization of chromium vi removal from aqueous solution using tamarind wood activated carbon in batch process
Journal of Hazardous Materials, 2009Co-Authors: J N Sahu, Jyotikusum Acharya, B C MeikapAbstract:Abstract The present paper discusses response surface methodology (RSM) as an efficient approach for Predictive model building and optimization of chromium adsorption on developed activated carbon. In this work the application of RSM is presented for optimizing the removal of Cr(VI) ions from aqua solutions using activated carbon as adsorbent. All experiments were performed according to statistical designs in order to develop the Predictive Regression models used for optimization. The optimization of adsorption of chromium on activated carbon was carried out to ensure a high adsorption efficiency at low adsorbent dose and high initial concentration of Cr(VI). While the goal of adsorption of chromium optimization was to improve adsorption conditions in batch process, i.e., to minimize the adsorbent dose and to increase the initial concentration of Cr(VI). In the adsorption experiments a laboratory developed Tamarind wood activated carbon made of chemical activation (zinc chloride) was used. A 2 4 full factorial central composite design experimental design was employed. Analysis of variance (ANOVA) showed a high coefficient of determination value ( R 2 = 0.928) and satisfactory prediction second-order Regression model was derived. Maximum chromium removal efficiency was predicted and experimentally validated. The optimum adsorbent dose, temperature, initial concentration of Cr(VI) and initial pH of the Cr(VI) solution were found to be 4.3 g/l, 32 °C, 20.15 mg/l and 5.41 respectively. Under optimal value of process parameters, high removal (>89%) was obtained for Cr(VI).
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response surface modeling and optimization of chromium vi removal from aqueous solution using tamarind wood activated carbon in batch process
Journal of Hazardous Materials, 2009Co-Authors: J N Sahu, Jyotikusum Acharya, B C MeikapAbstract:The present paper discusses response surface methodology (RSM) as an efficient approach for Predictive model building and optimization of chromium adsorption on developed activated carbon. In this work the application of RSM is presented for optimizing the removal of Cr(VI) ions from aqua solutions using activated carbon as adsorbent. All experiments were performed according to statistical designs in order to develop the Predictive Regression models used for optimization. The optimization of adsorption of chromium on activated carbon was carried out to ensure a high adsorption efficiency at low adsorbent dose and high initial concentration of Cr(VI). While the goal of adsorption of chromium optimization was to improve adsorption conditions in batch process, i.e., to minimize the adsorbent dose and to increase the initial concentration of Cr(VI). In the adsorption experiments a laboratory developed Tamarind wood activated carbon made of chemical activation (zinc chloride) was used. A 2(4) full factorial central composite design experimental design was employed. Analysis of variance (ANOVA) showed a high coefficient of determination value (R(2)=0.928) and satisfactory prediction second-order Regression model was derived. Maximum chromium removal efficiency was predicted and experimentally validated. The optimum adsorbent dose, temperature, initial concentration of Cr(VI) and initial pH of the Cr(VI) solution were found to be 4.3g/l, 32 degrees C, 20.15 mg/l and 5.41 respectively. Under optimal value of process parameters, high removal (>89%) was obtained for Cr(VI).
Peter C B Phillips - One of the best experts on this subject based on the ideXlab platform.
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robust econometric inference with mixed integrated and mildly explosive regressors
Journal of Econometrics, 2016Co-Authors: Peter C B Phillips, Ji Hyung LeeAbstract:Abstract This paper explores in several prototypical models a convenient inference procedure for nonstationary variable Regression that enables robust chi-square testing for a wide class of persistent and endogenous regressors. The approach uses the mechanism of self-generated instruments called IVX instrumentation developed by Magdalinos and Phillips (2009b). We first show that these methods remain valid for regressors with local unit roots in the explosive direction and mildly explosive roots, where the roots are further from unity in the explosive direction than O ( n − 1 ) . It is also shown that Wald testing procedures remain robust for multivariate regressors with certain forms of mixed degrees of persistence. These robustifications are useful in econometric inference, for example, when there are periods of mildly explosive trends in some or all of time series employed in the analysis but the exact knowledge on the regressor persistence is unavailable. Some aspects of the choice of the IVX instruments are investigated and practical guidance is provided but the issue of optimal IVX instrument choice remains unresolved. The methods are straightforward to apply in practical work such as Predictive Regression applications in finance.
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pitfalls and possibilities in Predictive Regression
Research Papers in Economics, 2015Co-Authors: Peter C B PhillipsAbstract:Financial theory and econometric methodology both struggle in formulating models that are logically sound in reconciling short run martingale behaviour for financial assets with predictable long run behavior, leaving much of the research to be empirically driven. The present paper overviews recent contributions to this subject, focussing on the main pitfalls in conducting Predictive Regression and on some of the possibilities offered by modern econometric methods. The latter options include indirect inference and techniques of endogenous instrumentation that use convenient temporal transforms of persistent regressors. Some additional suggestions are made for bias elimination, quantile crossing amelioration, and control of Predictive model misspecification.
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halbert white jr memorial jfec lecture pitfalls and possibilities in Predictive Regression
Journal of Financial Econometrics, 2015Co-Authors: Peter C B PhillipsAbstract:Financial theory and econometric methodology both struggle in formulating models that are logically sound in reconciling short-run martingale behavior for financial assets with predictable long-run behavior, leaving much of the research to be empirically driven. The present article overviews recent contributions to this subject, focusing on the main pitfalls in conducting Predictive Regression and on some of the possibilities offered by modern econometric methods. The latter options include indirect inference and techniques of endogenous instrumentation that use convenient temporal transforms of persistent regressors. Some additional suggestions are made for bias elimination, quantile crossing amelioration, and control of Predictive model misspecification.
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Nonparametric Predictive Regression
Journal of Econometrics, 2015Co-Authors: Ioannis Kasparis, Elena Andreou, Peter C B PhillipsAbstract:A unifying framework for inference is developed in Predictive Regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The limit distribution of these Predictive tests is nuisance parameter free and holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit root processes. Asymptotic theory and simulations show that the proposed tests are more powerful than existing parametric predictability tests when deviations from unity are large or the Predictive Regression is nonlinear. Empirical illustrations to monthly SP500 stock returns data are provided.
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on confidence intervals for autoregressive roots and Predictive Regression
Econometrica, 2014Co-Authors: Peter C B PhillipsAbstract:Local to unity limit theory is used in applications to construct confidence intervals (CIs) for autoregressive roots through inversion of a unit root test (Stock (1991)). Such CIs are asymptotically valid when the true model has an autoregressive root that is local to unity (ρ = 1 + c/n), but are shown here to be invalid at the limits of the domain of definition of the localizing coefficient c because of a failure in tightness and the escape of probability mass. Failure at the boundary implies that these CIs have zero asymptotic coverage probability in the stationary case and vicinities of unity that are wider than O(n−1/3). The inversion methods of Hansen (1999) and Mikusheva (2007) are asymptotically valid in such cases. Implications of these results for Predictive Regression tests are explored. When the Predictive regressor is stationary, the popular Campbell and Yogo (2006) CIs for the Regression coefficient have zero coverage probability asymptotically, and their Predictive test statistic Q erroneously indicates predictability with probability approaching unity when the null of no predictability holds. These results have obvious cautionary implications for the use of the procedures in empirical practice.
Ross D King - One of the best experts on this subject based on the ideXlab platform.
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new approach to pharmacophore mapping and qsar analysis using inductive logic programming application to thermolysin inhibitors and glycogen phosphorylase b inhibitors
Journal of Medicinal Chemistry, 2002Co-Authors: Nathalie Marchandgeneste, Kimberly A Watson, Bjorn K Alsberg, Ross D KingAbstract:A key problem in QSAR is the selection of appropriate descriptors to form accurate Regression equations for the compounds under study. Inductive logic programming (ILP) algorithms are a class of machine-learning algorithms that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives ILP the advantages of not requiring explicit superimposition of individual compounds in a dataset, of dealing naturally with multiple conformations, and of using a language much closer to that used normally by chemists. We unify ILP and standard Regression techniques to give a QSAR method that has the strength of ILP at describing steric structure with the familiarity and power of Regression methods. Complex pharmacophores, correlating with activity, were identified and used as new indicator variables, along with the comparative molecular field analysis (CoMFA) prediction, to form Predictive Regression equations. ...
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new approach to pharmacophore mapping and qsar analysis using inductive logic programming application to thermolysin inhibitors and glycogen phosphorylase b inhibitors
Journal of Medicinal Chemistry, 2002Co-Authors: Nathalie Marchandgeneste, Kimberly A Watson, Bjorn K Alsberg, Ross D KingAbstract:A key problem in QSAR is the selection of appropriate descriptors to form accurate Regression equations for the compounds under study. Inductive logic programming (ILP) algorithms are a class of machine-learning algorithms that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives ILP the advantages of not requiring explicit superimposition of individual compounds in a dataset, of dealing naturally with multiple conformations, and of using a language much closer to that used normally by chemists. We unify ILP and standard Regression techniques to give a QSAR method that has the strength of ILP at describing steric structure with the familiarity and power of Regression methods. Complex pharmacophores, correlating with activity, were identified and used as new indicator variables, along with the comparative molecular field analysis (CoMFA) prediction, to form Predictive Regression equations. We compared the formation of 3D-QSARs using standard CoMFA with the use of ILP on the well-studied thermolysin zinc protease inhibitor dataset and a glycogen phosphorylase inhibitor dataset. In each case the addition of ILP variables produced statistically better results (P < 0.01 for thermolysin and P < 0.05 for GP datasets) than the CoMFA analysis. Moreover, the new ILP variables were not found to increase the complexity of the final QSAR equations and gave possible insight into the binding mechanism of the ligand-protein complex under study.
Mark E Wohar - One of the best experts on this subject based on the ideXlab platform.
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do terror attacks predict gold returns evidence from a quantile Predictive Regression approach
Research Papers in Economics, 2016Co-Authors: Rangan Gupta, Mark E Wohar, Anandamayee Majumdar, Christian PierdziochAbstract:Much significant research has been done to study how terror attacks affect financial markets. We contribute to this research by studying whether terror attacks, in addition to standard predictors considered in earlier research, help to predict gold returns. To this end, we use a Quantile-Predictive-Regression (QPR) approach that accounts for model uncertainty and model instability. We find that terror attacks have Predictive value for the lower and especially for the upper quantiles of the conditional distribution of gold returns.
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structural breaks and Predictive Regression models of aggregate u s stock returns
Social Science Research Network, 2008Co-Authors: David E Rapach, Mark E WoharAbstract:In this article we examine the structural stability of Predictive Regression models of U.S. quarterly aggregate real stock returns over the postwar era. We consider Predictive Regressions models of S&P 500 and CRSP equal-weighted real stock returns based on eight financial variables that display Predictive ability in the extant literature. We test for structural stability using the popular Andrews SupF statistic and the Bai subsample procedure in conjunction with the Hansen heteroskedastic fixed-regressor bootstrap. We also test for structural stability using the recently developed methodologies of Elliott and Muller, and Bai and Perron. We find strong evidence of structural breaks in five of eight bivariate Predictive Regression models of S&P 500 returns and some evidence of structural breaks in the three other models. There is less evidence of structural instability in bivariate Predictive Regression models of CRSP equal-weighted returns, with four of eight models displaying some evidence of structural breaks. We also obtain evidence of structural instability in a multivariate Predictive Regression model of S&P 500 returns. When we estimate the Predictive Regression models over the different regimes defined by structural breaks, we find that the Predictive ability of financial variables can vary markedly over time.
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structural breaks and Predictive Regression models of aggregate u s stock returns
Journal of Financial Econometrics, 2006Co-Authors: David E Rapach, Mark E WoharAbstract:In this article we examine the structural stability of Predictive Regression models of U.S. quarterly aggregate real stock returns over the postwar era. We consider Predictive Regressions models of S&P 500 and CRSP equal-weighted real stock returns based on eight financial variables that display Predictive ability in the extant literature. We test for structural stability using the popular Andrews SupF statistic and the Bai subsample procedure in conjunction with the Hansen heteroskedastic fixed-regressor bootstrap. We also test for structural stability using the recently developed methodologies of Elliott and Muller, and Bai and Perron. We find strong evidence of structural breaks in five of eight bivariate Predictive Regression models of S&P 500 returns and some evidence of structural breaks in the three other models. There is less evidence of structural instability in bivariate Predictive Regression models of CRSP equal-weighted returns, with four of eight models displaying some evidence of structural breaks. We also obtain evidence of structural instability in a multivariate Predictive Regression model of S&P 500 returns. When we estimate the Predictive Regression models over the different regimes defined by structural breaks, we find that the Predictive ability of financial variables can vary markedly over time. Copyright 2006, Oxford University Press.